As known, capacity planning is very crucial for the efficient management of available resources. In the context of IT infrastructure, it is the science of estimating the space, the computer hardware, software and connection resources that will be needed in some time in the future. The aim of a capacity planner is therefore to find the most cost efficient solution by determining appropriate tradeoffs so that the needed technical capacity/resources can be added in time to meet the predicted demand, however making sure that the resources do not go unused for long periods of time. In other words, it is required to upgrade or update some hardware at the right point in time, so as to cope with the demand without too much anticipate the correct time so as to budget correctly upgrading costs.
Time series analysis is a very vital process for predicting how major aspects of various economic and social processes evolve over time. For a long time now, it is extensively applied in predicting the growth of key business activities, for instance the rise and fall of stock prices, determining market trends amongst others. Due to the rising need of optimizing IT infrastructure to offer better services while minimizing the cost of maintaining and buying the infrastructure, there is a growing necessity of developing advanced methods that automatically trigger hardware upgrading or add-on processes.
Time series analysis applied to an IT infrastructure is based on collecting or sampling data related to signals issued by monitored hardware, so as to build the historical behaviour and hence estimate the future points of the model. This analysis projected in time, is apt to supply specific information for establishing when and how said hardware or software resource will require upgrading or substitution. Upgrading of a certain resource n the IT infrastructure for example intended for a specific task, may occur also as an automatic re-allocation of resources (for example memory banks, disk space, CPU, . . . ) from an other system provisionally allocated to a different task: in such a case the entire upgrading process can be carried out in a completely automatic mode.
The same analysis supplies information about occurrence of events, errors on prediction bands, point in time when given hardware changes should be done or when the given infrastructure will breakdown.
As an example, the following can be reported: based on past behaviour of entities like the number of accesses to or transactions in a web site, a time series analysis can help minimizing user response time by predicting future hardware requests. This constitutes a simple capacity planning situation in a demand-supply scenario where a balance between how much hardware infrastructures need to be installed on the basis of expected number of users and minimizing the loss of profit situations due to a slow web access needs to be determined by a capacity planner.
One of the algorithm mostly employed in the field of time series prediction is the well known Box and Jenkins prediction algorithm (see, for example, G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, Calif.: Holden-Day, 1976 and J. G. Caldwell, (2007, February), Mathematical forecasting using the Box-Jenkins methodology; this system is able to roughly match well to operate to any condition, regardless of the specific domain wherein it is used. Typically, to tune this algorithm to supply good results for a specific application field, it is required a certain amount of manual intervention to select a number of tuning parameters based on visual observation of the historical behaviour of the specific acquired time series. Of course, this way of proceeding, as such, is not suitable to completely automate the upgrading process.
An object of the present invention it is hence that of supplying a method for hardware upgrading based on a robust time series, prediction in the domain of capacity planning of business and workload performance metrics in IT infrastructure, like business drivers, technical proxy, CPU, memory utilization etc. To achieve the goal, it is desired to develop a completely automated time series prediction method. Having an automated method for performance data, has two-fold advantages: (i) due to the large volumes of data with constantly changing physical characteristics which needs to be regularly analyzed, an automation of reading data, updating of internal parameters and a through extensive analysis is imperative; (ii) human intervention in time series prediction process always has some draw backs as capacity planners are engineers who generally lack a deep mathematical and statistical knowledge that time forecasting experts have.